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Machine Learning Description of Excited State Dynamics in Small Organic Molecules
Machine Learning Description of Excited State Dynamics in Small Organic Molecules
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152026
- ISBN
- 9798383703137
- DDC
- 542
- 서명/저자
- Machine Learning Description of Excited State Dynamics in Small Organic Molecules
- 발행사항
- [Sl] : University of Minnesota, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 129 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Goodpaster, Jason D.
- 학위논문주기
- Thesis (Ph.D.)--University of Minnesota, 2024.
- 초록/해제
- 요약Machine learning offers a method to assess systems at a highly accurate level comparable to electronic structure methods for a fraction of the computational cost. This work focuses on the sampling of molecular potential energy surfaces for the creation of data sets to train machine learning models. Chapter 2 seeks to model equilibrium between species in the nitric oxide formation reaction and use grand canonical Monte Carlo to model this reaction. While nitrogen and oxygen molecules were successfully sampled, discontinuities in the density functional theory and complete active space self-consistent field potential energy surfaces prohibited successful modeling of nitric oxide. Chapter 3 seeks to model pathway-based intramolecular reactivity between ethylene and ethylidene in their first excited state. This was approached by using normal mode sampling along nudged elastic band paths, along with configurations from network-driven molecular dynamics simulations selected via query-by-committee combined with a relative energy cutoff. It was found that these techniques were a useful supplementary data-gathering technique that successfully described reaction barrier energies to within 1.5 kcal/mol, but were unable to sample relevant regions of phase space required to reproduce correct molecular motion. Chapter 4 uses a classical force field in molecular dynamics simulations to provide theoretical insight into thermodynamic drives of a modified histidine substrate for Histidine Kinase that would be able to probe enzyme activity directly. Findings supported proteomics surveys indicating glutamate residue 253 provides the most thermodynamically accessible target for the modified histidine in diazirine form.
- 일반주제명
- Computational chemistry
- 일반주제명
- Molecular physics
- 일반주제명
- Chemistry
- 키워드
- Dynamics
- 키워드
- Machine learning
- 키워드
- Sampling
- 기타저자
- University of Minnesota Chemistry
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798383703137
■035 ▼a(MiAaPQ)AAI31333297
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a542
■1001 ▼aJohannesen, Andrew M.
■24510▼aMachine Learning Description of Excited State Dynamics in Small Organic Molecules
■260 ▼a[Sl]▼bUniversity of Minnesota▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a129 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Goodpaster, Jason D.
■5021 ▼aThesis (Ph.D.)--University of Minnesota, 2024.
■520 ▼aMachine learning offers a method to assess systems at a highly accurate level comparable to electronic structure methods for a fraction of the computational cost. This work focuses on the sampling of molecular potential energy surfaces for the creation of data sets to train machine learning models. Chapter 2 seeks to model equilibrium between species in the nitric oxide formation reaction and use grand canonical Monte Carlo to model this reaction. While nitrogen and oxygen molecules were successfully sampled, discontinuities in the density functional theory and complete active space self-consistent field potential energy surfaces prohibited successful modeling of nitric oxide. Chapter 3 seeks to model pathway-based intramolecular reactivity between ethylene and ethylidene in their first excited state. This was approached by using normal mode sampling along nudged elastic band paths, along with configurations from network-driven molecular dynamics simulations selected via query-by-committee combined with a relative energy cutoff. It was found that these techniques were a useful supplementary data-gathering technique that successfully described reaction barrier energies to within 1.5 kcal/mol, but were unable to sample relevant regions of phase space required to reproduce correct molecular motion. Chapter 4 uses a classical force field in molecular dynamics simulations to provide theoretical insight into thermodynamic drives of a modified histidine substrate for Histidine Kinase that would be able to probe enzyme activity directly. Findings supported proteomics surveys indicating glutamate residue 253 provides the most thermodynamically accessible target for the modified histidine in diazirine form.
■590 ▼aSchool code: 0130.
■650 4▼aComputational chemistry
■650 4▼aMolecular physics
■650 4▼aChemistry
■653 ▼aDynamics
■653 ▼aElectronic structure
■653 ▼aMachine learning
■653 ▼aSampling
■653 ▼aMolecular dynamic
■690 ▼a0219
■690 ▼a0609
■690 ▼a0485
■71020▼aUniversity of Minnesota▼bChemistry.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0130
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162557▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
